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Toyota had mature lean capabilities when developing its supplier network. This paper aims to explore how companies can develop a Toyota-style supplier network (TSN) while their lean capabilities are still evolving.

Theoretically, this paper relies on the literature on lean maturity levels and lean supplier network development. Empirically, the paper portrays a Toyota-style initiative, detailing the buyer’s efforts to develop internal lean capabilities concurrently with developing lean in its supplier network. It compares the Network for supplier innovation (NSI) initiative with TSN development regarding activities, organizations and knowledge-sharing routines.

Unlike the sequential development in the case of Toyota, NSI improved performance and capabilities in the buyer’s supplier network by implementing lean in the firm and its supplier network concurrently. Third-party involvement was the key to the initiative’s success.

The findings are based on an in-depth single-case study which allows theoretical generalization but not statistical generalization. Furthermore, the case study concerns an initiative with Norwegian firms during a financial recession. Future studies should consider these limitations on how firms with evolving lean capabilities can develop a TSN-style supplier network and the importance of involving third parties operating in the role of lean master.

This study suggests what buying firms should consider when designing a TSN initiative, enrolling suppliers and engaging third parties that can take on the role of lean master.

Previous research has focused on how mature lean firms develop lean suppliers and networks. This paper extends this to firms whose lean capabilities are still evolving.

This study aims to investigate the implementation of lean human resource management (HRM) practices in manufacturing small- and medium-sized enterprises (SMEs) and explore how various factors interact to influence their successful adoption. By exploring the interplay among these factors, the research seeks to identify key drivers affecting the adoption of lean HRM in manufacturing SMEs. Ultimately, the research intends to provide insights that can guide organisations, practitioners and policymakers in effectively implementing lean HRM practices to enhance operational efficiency, workforce engagement and competitiveness within the manufacturing SME sector.

The study combined total interpretive structural modelling (TISM) and Matrice d'Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis. TISM helped in understanding the hierarchical relationship among different factors influencing lean HRM implementation, whereas MICMAC analysis provided insights into the level of influence and dependence of each factor on others.

The research revealed that “top management support” emerged as the most independent factor, indicating that strong support from top management is crucial for initiating and sustaining lean HRM practices in manufacturing SMEs. On the other hand, “employee involvement and empowerment” was identified as the most dependent factor, suggesting that fostering a culture of employee engagement and empowerment greatly relies on the successful implementation of lean HRM practices.

While the study provided valuable insights, it has certain limitations. The research was conducted within the specific context of manufacturing SMEs, which might limit the generalizability of the findings to other industries. Expert opinions introduce subjectivity in data collection. Additionally, the study may not cover all critical factors, allowing room for further exploration in future research.

The findings have practical implications for manufacturing SMEs aiming to implement lean HRM practices. Recognising the pivotal role of top management support, organisations should invest in cultivating a strong leadership commitment to lean HRM initiatives. Furthermore, enhancing employee involvement and empowerment can lead to better adoption of lean HRM practices, resulting in improved operational efficiency and overall competitiveness.

This research contributes to the field by offering a comprehensive exploration of the interplay among factors influencing lean HRM implementation. The use of TISM and MICMAC analysis provides a unique perspective on the relationship dynamics between these factors, allowing for a nuanced understanding of their roles in the adoption of lean HRM practices in manufacturing SMEs. The identification of “top management support” as the most independent and “employee involvement and empowerment” as the most dependent factors adds original insights to the existing literature.

With hybrid simulation techniques getting popular for systems improvement in multiple fields, this study aims to provide insight on the use of hybrid simulation to assess the effect of lean manufacturing (LM) techniques on manufacturing facilities and the transition of a mass production (MP) facility to incorporating LM techniques.

In this paper, the authors apply a hybrid simulation approach to improve an educational automotive assembly line and provide guidelines for implementing different LM techniques. Specifically, the authors describe the design, development, verification and validation of a hybrid discrete-event and agent-based simulation model of a LEGO® car assembly line to analyze, improve and assess the system’s performance. The simulation approach examines the base model (MP) and an alternative scenario (just-in-time [JIT] with Heijunka).

The hybrid simulation approach effectively models the facility. The alternative simulation scenario (implementing JIT and Heijunka LM techniques) improved all examined performance metrics. In more detail, the system’s lead time was reduced by 47.37%, the throughput increased by 5.99% and the work-in-progress for workstations decreased by up to 56.73%.

This novel hybrid simulation approach provides insight and can be potentially extrapolated to model other manufacturing facilities and evaluate transition scenarios from MP to LM.

This paper aims to introduce a model using a digital twin concept in a cold heading manufacturing and develop a digital visual management (VM) system using Lean overall equipment effectiveness (OEE) tool to enhance the process performance and establish Fourth Industrial Revolution (I4.0) platform in small and medium enterprises (SMEs).

This work utilised plan, do, check, act Lean methodology to create a digital twin of each machine in a smart manufacturing facility by taking the Lean tool OEE and digitally transforming it in the context of I4.0. To demonstrate the effectiveness of process digitisation, a case study was carried out at a manufacturing department to provide the data to the model and later validate synergy between Lean and I4.0 platform.

The OEE parameter can be increased by 10% using a proposed digital twin model with the introduction of a Level 0 into VM platform to clearly define the purpose of each data point gathered further replicate in projects across the value stream.

The findings suggest that researchers should look beyond conversion of stored data into visualisations and predictive analytics to improve the model connectivity. The development of strong big data analytics capabilities in SMEs can be achieved by shortening the time between data gathering and impact on the model performance.

The novelty of this study is the application of OEE Lean tool in the smart manufacturing sector to allow SME organisations to introduce digitalisation on the back of structured and streamlined principles with well-defined end goals to reach the optimal OEE.

We studied the relationship between job engagement and systematic problem solving (SPS) among shop-floor employees and how lean production (LP) and Internet of Things (IoT) systems moderate this relationship.

We collected data from a sample of 440 shop floor workers in 101 manufacturing work units across 33 plants. Because our data is nested, we employed a series of multilevel regression models to test the hypotheses. The application of IoT systems within work units was evaluated by our research team through direct observations from on-site visits.

Our findings indicate a positive association between job engagement and SPS. Additionally, we found that the adoption of lean bundles positively moderates this relationship, while, surprisingly, the adoption of IoT systems negatively moderates this relationship. Interestingly, we found that, when the adoption of IoT systems is complemented by a lean management system, workers tend to experience a higher effect on the SPS of their engagement.

One limitation of this research is the reliance on the self-reported data collected from both workers (job engagement, SPS and control variables) and supervisors (lean bundles). Furthermore, our study was conducted in a specific country, Italy, which might have limitations on the generalizability of the results since cross-cultural differences in job engagement and SPS have been documented.

Our findings highlight that employees’ strong engagement in SPS behaviors is shaped by the managerial and technological systems implemented on the shop floor. Specifically, we point out that implementing IoT systems without the appropriate managerial practices can pose challenges to fostering employee engagement and SPS.

This paper provides new insights on how lean and new technologies contribute to the development of learning-to-learn capabilities at the individual level by empirically analyzing the moderating effects of IoT systems and LP on the relationship between job engagement and SPS.

The purpose of this study is to propose a new mapping tool called Circular Value Stream Mapping (C-VSM) that combines Circular Economy principles with Lean tools to enhance sustainability performance in operations.

To develop the C-VSM tool, the researchers conducted a literature review and a focus group. The tool was then applied to two real case studies in the agri-food sector, specifically analyzing an artichoke and olive oil producer, to assess its validity and effectiveness.

The study introduces the Circular Resource Box (CRB) as a key innovation in the C-VSM tool. This visual representation effectively captures resource circularity and how resources and wastes are managed, making it easy to identify circularity in the production process. By combining qualitative and quantitative information with this visual representation, companies can identify improvement opportunities aligned with the CE.

The research is limited in scope as it focuses on the application of the C-VSM tool in the agri-food sector. Further research could explore its applicability in other industries and settings to understand its broader impact.

The C-VSM tool provides practical benefits to companies seeking to transition from linear to circular production processes. It enables practitioners to identify opportunities to reduce environmental impacts and optimize production operations in line with CE.

The introduction of the C-VSM tool is a novel approach that bridges the gap between Lean Manufacturing and CE concepts, advancing the understanding of how CE thinking can be effectively implemented in operations.

To address the absence of Lean in transportation logistics in the digital era, this study aims to investigate the application of Lean transportation (LT) tools to reduce waste and facilitate the digital transformation of dedicated road transportation in the offshore industry.

The study adopts action research with a multimethod approach, including a scoping review, focus groups (FG) and participant observation. The research is conducted within the offshore supply chain of a major oil and gas company.

Implementing LT’s continuous improvement tools, particularly value stream mapping (VSM), reduces offshore transportation waste and provides empirical evidence about the intersection of Lean and digital technologies. Applying techniques drawn from organisational learning theory (OLT), stakeholders involved in VSM mapping and FGs engage in problem-solving and develop action plans, driving digital transformation. Waste reduction in loading and unloading stages leads to control actions, automation and process improvements, significantly reducing downtime. This results in an annual monetary gain of US$1.3m. The study also identifies waste related to human effort and underutilised digital resources.

This study contributes to theory and practice by using action research and LT techniques in a real intervention case. From the lens of OLT, it highlights the potential of LT tools for digital transformation and demonstrates the convergence of waste reduction through Lean and Industry 4.0 technologies in the offshore supply chain. Practical outputs, including a benchmarking questionnaire and a plan-do-check-act cycle, are provided for other companies in the same industry segment.

This paper uses the complex proportionality assessment (COPRAS) method to examine the driving factors of Industry 4.0 (I4) technologies for lean implementation in small and medium-sized enterprises (SMEs).

Adopting I4 technology is imperative for SMEs seeking to maintain competitiveness within the manufacturing sector. A thorough understanding of the driving factors involved is required to support the implementation of I4. For this objective, the multi-criteria decision-making (MCDM) tool COPRAS was used to efficiently analyze and rank these driving elements based on their importance. These factors can help small and medium-sized firms (SMEs) prioritize their efforts and investments in I4 technologies for lean implementation.

This study evaluates and prioritizes the nine I4 factors according to the perceptions of SMEs. The ranking offers significant insights into the factors SMEs consider more accessible and effective when adopting I4 technologies.

The author's original contribution is to examine I4 driving factors for lean implementation in SMEs using COPRAS.

The purpose of this paper is to examine the digitalization of operational processes and activities in lean manufacturing firms and explore the associated learning implications through the lens of cumulative capability theory.

Adopting a multiple-case design, we examine four cases of digitalization initiatives within lean manufacturing firms. We collected data through semi-structured interviews and direct observations during site visits.

The study uncovers the development of learning capabilities as a result of integrating lean and digitalization. We find that digitalization in lean manufacturing firms contributes to the development of both routinized and evolutionary learning capabilities in a cumulative fashion.

The study adds nuance to the limited theoretical understanding of the integration of lean and digitalization by showing how it cumulatively develops the learning capabilities of lean manufacturing firms. As such, the study supports the robustness of cumulative capability theory. We further contribute to research by offering empirical support for the cumulative nature of learning.

Prior to managing a company’s processes in the presence of a combination of paradigms, there is a need to understand their underlying interaction. This paper systematically reviews the existing literature that discusses the interaction between lean production (LP) and the fourth industrial revolution (i.e. Industry 4.0). The study aims to understand how the interaction unfolds and whether it is synergistic.

The research relies on a systematic literature review of peer-reviewed articles from Scopus and Web of Science that discuss the interaction between the two paradigms. The final set of articles pertaining to the topic was analysed.

The article presents that the interaction between the two paradigms occurs through a representation of the pillars of the House of Lean (HoL) interacting with the nine technological pillars of Industry 4.0. There is a consensus on the synergistic nexus among the pillars and their positive impact on operational performance. We also demonstrate the weights of the interactions between the two paradigms and the areas of operations management where this interaction takes place through Sankey charts. Our research indicates that the largest synergistic interaction occurs between just-in-time and industrial Internet of Things (IIoT) and that companies should invest in IoT and cyber-physical systems as they have the greatest weight of interactions with the pillars of the HoL.

This research facilitates a deeper insight into the interaction between LP and Industry 4.0 by organising and discussing existing research on the subject matter. It serves as a starting point for future researchers to formulate hypotheses about the interaction among the various pillars of LP and Industry 4.0, apply these interactions and test them through empirical research.

It could serve as a guide for managers to understand with which interactions they should start the digitalisation process.

With the rise in discussions on the interaction between the two paradigms, there is still an opportunity to understand the specificity of this interaction. Compared to the initial seminal works on the subject, such as Buer et al. (2018b), which investigated the direction of interaction between the two paradigms, this research contributes to further investigating this specificity and gaining a better understanding of the relationship governing the interaction between LP and Industry 4.0 by delineating the interaction state among the pillars of the two paradigms and its relevant importance.